PHD Computer information systems COURSES

These six PhD seminars are offered in the fall, winter and spring quarters, with topics selected from the following: decision-support systems, economics of information and the valuation of information systems, issues in the management of information systems and the economics of computing, advanced topics in systems analysis and design, organizational aspects of information systems, logical and physical database design and topics discussed in the joint CIS/OMG PhD seminars.

AEC 520. CAUSAL INFERENCE

The course will cover how to design compelling research, the focus of which is causal inference. The course covers the design of true experiments and concepts of validity (internal validity, external validity, replicability). The approach should follow the Rubin potential outcomes framework. The course then covers causal inference and related econometric methods in observational studies for cross-sectional, panel data, and time-series, and non-linear models including OLS, instrumental variables, Heckman selection models, regression discontinuity designs, matched samples designs, granger causality, event studies, diff-in-diff, fixed effects, clustering standard errors, dynamic panel methods (e.g., Blundell and Bond 1998), and some issues in logit/probit/multinomial logit. Although the course will discuss many econometric techniques, students are expected to have already learned the mechanics of these methods, so that the course can focus on causal inference and its limitations in these methodologies.